Papers with Code 아카이브

Medical Image Segmentation

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하위 태스크: 31
평가표: 49
보존된 평가 기록: 332

Medical Image Segmentation is a computer vision task that involves dividing an medical image into multiple segments, where each segment represents a different object or structure of interest in the image. The goal of medical image segmentation is to provide a precise and accurate representation of the objects of interest within the image, typically for the purpose of diagnosis, treatment planning, and quantitative analysis.

<span style="color:grey; opacity: 0.6">( Image credit: IVD-Net )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/dyxu-cuhkcse/smg-learning

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Towards Synchronous Memorizability and Generalizability with Site-Modulated Diffusion Replay for Cross-Site Continual Segmentation

https://github.com/eatbeanss/CSWin-UNet

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CSWin-UNet: Transformer UNet with Cross-Shaped Windows for Medical Image Segmentation

https://github.com/eceisik/FeTS_Challenge_METU_FL_Team

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Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentation

https://github.com/eduardchelebian/sefi

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Segmentation-free integration of nuclei morphology and spatial transcriptomics for retinal images

https://github.com/eerdil/task_agnostic_ood

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Task-agnostic Out-of-Distribution Detection Using Kernel Density Estimation

https://github.com/elchiou/DA

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Harnessing Uncertainty in Domain Adaptation for MRI Prostate Lesion Segmentation

https://github.com/elitap/classimbalance

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Tackling the Class Imbalance Problem of Deep Learning Based Head and Neck Organ Segmentation

https://github.com/eltzanis/maistro

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mAIstro: an open-source multi-agentic system for automated end-to-end development of radiomics and deep learning models for medical imaging

https://github.com/emeraldsnrpu/attention-based-3d-resunet

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A Unified Multi-Scale Attention-Based Network for Automatic 3D Segmentation of Lung Parenchyma & Nodules In Thoracic CT Images

https://github.com/endo-angel/ct-angel

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Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients

https://github.com/endoluminalsurgicalvision-imr/acnorm

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AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative Normalization

https://github.com/endoluminalsurgicalvision-imr/ccfv

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Pick the Best Pre-trained Model: Towards Transferability Estimation for Medical Image Segmentation

https://github.com/endoluminalsurgicalvision-imr/ismm

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ReshapeIT: Reliable Shape Interaction with Implicit Template for Anatomical Structure Reconstruction

https://github.com/endoluminalsurgicalvision-imr/pass

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PASS:Test-Time Prompting to Adapt Styles and Semantic Shapes in Medical Image Segmentation

https://github.com/erashed/SubForkNet

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End-to-end semantic segmentation of personalized deep brain structures for non-invasive brain stimulation

https://github.com/erjian96/clcs

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Imbalanced Medical Image Segmentation with Pixel-dependent Noisy Labels

https://github.com/erneternet/ernet

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ERNet: Unsupervised Collective Extraction and Registration in Neuroimaging Data

https://github.com/esgomezm/microscopy-dl-suite-tf

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Search for temporal cell segmentation robustness in phase-contrast microscopy videos

https://github.com/etjoa003/medical_imaging

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Enhancing the Extraction of Interpretable Information for Ischemic Stroke Imaging from Deep Neural Networks

https://github.com/eyened/rtnls_vascx_models

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VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Images

https://github.com/ezequieldlrosa/isles22

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ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset

https://github.com/fahadshamshad/awesome-transformers-in-medical-imaging

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Transformers in Medical Imaging: A Survey

https://github.com/fahimzaman/ldseg

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Latent Diffusion for Medical Image Segmentation: End to end learning for fast sampling and accuracy

https://github.com/faildeny/fednnunet

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Federated nnU-Net for Privacy-Preserving Medical Image Segmentation

https://github.com/fangzhuoyi/xlstm-vmunet

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When Mamba Meets xLSTM: An Efficient and Precise Method with the xLSTM-VMUNet Model for Skin lesion Segmentation

https://github.com/faresbougourzi/awesome-dl-for-medical-imaging-segmentation

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Recent Advances in Medical Imaging Segmentation: A Survey

https://github.com/faresbougourzi/d-trattunet

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D-TrAttUnet: Toward Hybrid CNN-Transformer Architecture for Generic and Subtle Segmentation in Medical Images

https://github.com/faresbougourzi/pagtransynet

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Rethinking Attention Gated with Hybrid Dual Pyramid Transformer-CNN for Generalized Segmentation in Medical Imaging

https://github.com/farzad-bz/harmonizing-flows

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Harmonizing Flows: Unsupervised MR harmonization based on normalizing flows

https://github.com/faustomilletari/VNet

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V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

https://github.com/faysal-md/an-interpretable-deep-learning-approach-for-skin-cancer-categorization-ieee2023

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An Interpretable Deep Learning Approach for Skin Cancer Categorization

https://github.com/fazhan-cs/dcp

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Dual-Teacher Ensemble Models with Double-Copy-Paste for 3D Semi-Supervised Medical Image Segmentation

https://github.com/federicospagnolo/IES

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Instance-level quantitative saliency in multiple sclerosis lesion segmentation

https://github.com/feevos/ssg2

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SSG2: A new modelling paradigm for semantic segmentation

https://github.com/fenghetan9/cmu-net

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CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network

https://github.com/fenghetan9/cmunext

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CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.